A gadoxetic acid-enhanced MRI-based model using LI-RADS v2018 features for preoperatively predicting Ki-67 expression in hepatocellular carcinoma

Purpose To construct a gadoxetic acid-enhanced MRI (EOB-MRI) -based multivariable model to predict Ki-67 expression levels in hepatocellular carcinoma (HCC) using LI-RADS v2018 imaging features. Methods A total of 121 patients with HCC who underwent EOB-MRI were enrolled in this study. The patients were divided into three groups according to Ki-67 cut-offs: Ki-67 ≥ 20% (n = 86) vs. Ki-67 < 20% (n = 35); Ki-67 ≥ 30% (n = 73) vs. Ki-67 < 30% (n = 48); Ki-67 ≥ 50% (n = 45) vs. Ki-67 < 50% (n = 76). MRI features were analyzed to be associated with high Ki-67 expression using logistic regression to construct multivariable models. The performance characteristic of the models for the prediction of high Ki-67 expression was assessed using receiver operating characteristic curves. Results The presence of mosaic architecture (p = 0.045), the presence of infiltrative appearance (p = 0.039), and the absence of targetoid hepatobiliary phase (HBP, p = 0.035) were independent differential factors for the prediction of high Ki-67 status (≥ 50% vs. < 50%) in HCC patients, while no features could predict high Ki-67 status with thresholds of 20% (≥ 20% vs. < 20%) and 30% (≥ 30% vs. < 30%) (p > 0.05). Four models were constructed including model A (mosaic architecture and infiltrated appearance), model B (mosaic architecture and targetoid HBP), model C (infiltrated appearance and targetoid HBP), and model D (mosaic architecture, infiltrated appearance and targetoid HBP). The model D yielded better diagnostic performance than the model C (0.776 vs. 0.669, p = 0.002), but a comparable AUC than model A (0.776 vs. 0.781, p = 0.855) and model B (0.776 vs. 0.746, p = 0.076). Conclusions Mosaic architecture, infiltrated appearance and targetoid HBP were sensitive imaging features for predicting Ki-67 index ≥ 50% and EOB-MRI model based on LI-RADS v2018 features may be an effective imaging approach for the risk stratification of patients with HCC before surgery.


Introduction
Hepatocellular carcinoma (HCC) is the most common primary liver cancer and ranks second in cancer-related mortality worldwide [1].High tumor recurrence and metastasis, which occurs in approximately 60%-70% of patients within 5 years, remains a major concern in HCC treatment [2][3][4][5].Patients with the same types of tumors receiving the same treatments at the same doses may have different outcomes due to differences in the proliferative activities of tumors.
Ki-67 is a nuclear antigen that is only expressed during the cell proliferation phase and has a short half-life [6][7][8].As such, it is an effective biomarker to predict tumor cell division and proliferative activity, which is believed to be associated with the therapeutic effects and prognoses of malignant tumors in clinical practice [6][7][8][9].The optimal cut-off value of Ki-67 to guide the clinical management of patients with HCC remains undetermined, although previous studies have shown that high Ki-67 expression is associated with tumor differentiation, lymph node metastasis, and poor prognoses [6,10].Currently, Ki-67 can only be evaluated by surgery or biopsy histopathology, which are invasive and may cause infection, intra-abdominal bleeding, and tumor spread [11].In addition, puncture biopsy has a high rate of misdiagnosis due to sampling error.Therefore, there is an urgent need for a non-invasive method to predict the optimal cut-off value of Ki-67 for the risk stratification of patients with HCC.
Gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) can play an essential role in the diagnosis, staging, and surveillance of HCC.To standardize the interpretation of features in imaging reports and promote communication between different HCCrelated disciplines, the Liver Imaging Reporting and Data System (LI-RADS) was introduced in 2016 by the American College of Radiology [12].Recent metaanalysis reported that LI-RADS showed moderate sensitivity of 62-67% and high specificity of 91-93% for diagnosing HCC [13,14].This high specificity at the cost of sensitivity was designed for the prevention of misallocation of liver transplants.Emerging pieces of studies suggested that EOB-MRI has high clinical value for preoperatively predicting Ki-67 expression in HCC, however, the clinical promotion has been limited because the commercial software is required to transform the results [15][16][17][18].
Thus, this study aimed to explore the correlation between EOB-MRI LI-RADS v2018 features and different Ki-67 expression levels, and construct a multivariable model based on EOB-MRI using LI-RADS v2018 features for preoperative prediction of Ki-67 expression in patients with HCC.

Patients
This retrospective single-center study was approved by the Institutional Review Board with waived requirement for informed consent (Ethical Board Approval Number: "K-2022-004-01").From January 2017 to April 2023, all patients with pathologically confirmed HCC in our hospital were included in this study.136 patients were excluded from the study; 67 were due to incomplete pathological data, 36 had previous treatment for HCC, 27 were due to incomplete MRI sequence, and the other 6 were due to poor quality of MRI images caused by respiratory motion artifacts.

Clinical and laboratory data
All clinical and laboratory data of the patient were retrieved and collected from the clinical case system.The characteristics including age, gender, etiology of the underlying liver disease, Child-pugh score, the levels of alpha-fetoprotein (AFP), alanine aminotransferase (ALT), serum total bilirubin (STB), plasma albumin (PA), and platelet levels were selected for distinguishing HCC with different thresholds of Ki-67.

Image analysis
All images were evaluated by two abdominal radiologists (with 10 and 17 years of experience, respectively) independently, who were blinded to the final pathological diagnosis.Inter-observer agreement was assessed, and any discrepancies were resolved by consensus as the reference standard.
LI-RADS v2018, including major (non-rim arterial phase hyperenhancement [APHE], non-peripheral washout, enhancing capsule and threshold growth), ancillary (favoring HCC in particular, and favoring malignancy, not HCC in particular), and LR-M imaging criteria (targetoid appearance and non-targetoid LR-M features) were used to evaluate all lesions [21].The threshold growth was not applicable because only one MRI examination per patient was evaluated in the analysis.Other imaging features, including intratumoral arteries (continuous enhancement of arterial vessels in the tumor during the arterial phase which attenuated in the portal phase and later phase), satellite nodules(presence of nodules ≤2 cm in diameter and within 2 cm of primary tumor), peritumoral enhancement(irregular enhancement outside the tumor margin in arterial phase which attenuated in portal phase and later phase), lymph node metastasis (the short axis of a lymph node was greater than 10 mm or central necrosis was found on MRI), portal and hepatic vein tumor thrombus (unequivocal enhancing soft tissue in portal and hepatic veins), and ascites, were also evaluated which has been reported in our previous study [22].LI-RADS category of each lesion was assigned by the same two abdominal radiologists.In addition, the largest tumor was evaluated in patients with multiple tumors.

Histopathological examination
The pathological reports of all included patients with HCC were retrospectively reviewed.The Ki-67 proliferation index was evaluated according to the normal immunohistochemical process and evaluated blindly by two experienced pathologists blindly.

Statistical analysis
Continuous variables were compared using student's t test or the Mann-Whitney U test and categorical variables were compared using χ 2 test or Fisher's exact test.Kappa (k) statistics were used to evaluate the agreement for MRI features (poor, 0.00-0.20;fair, 0.21-0.40;moderate, 0.41-0.60;substantial, 0.61-0.80;and excellent, 0.81-1.00).Data from the most experienced radiologist were used for analyses.
Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for predicting Ki-67 expression.Univariate predictors with p < 0.1 were used in the multivariate regression analysis.
Subsequently, different logistic regression models were built based on MRI features.For assessment of the discriminative abilities of the parameters and models, receiver operating characteristic (ROC) curves were constructed, and the areas under the ROC curves (AUC) were computed.The DeLong test was performed to compare AUCs of the prediction models.Two-sided p < 0.05 were considered statistically significant.All statistical tests were performed using SPSS (version 19.0, SPSS, Chicago, IL, USA).

Univariate and multivariate analysis of differential factors between different Ki-67 cut-offs
The relationship between EOB-MRI features and the different Ki-67 cut-offs are presented in Tables 2 and 3.
For Ki-67 ≥ 20%, univariate analysis suggested that absence of nonperipheral washout (p = 0.062), presence of mosaic architecture (p = 0.092), presence of rim APHE (p = 0.020), presence of targetoid restriction (p = 0.020), and presence of peritumoral enhancement (p = 0.026) were potential differential factors for the prediction of HCC with Ki-67 ≥ 20%.Subsequently, multivariate logistic analysis was conducted on these potential differential factors; no factors were significantly different.The interobserver agreement for each feature is described with Kappa (k) statistics For Ki-67 ≥ 30%, univariate analysis suggested that the presence of nodule-in-nodule architecture (p = 0.069), presence of mosaic architecture (p = 0.060), presence of coronal enhancement (p = 0.045), presence of rim APHE (p = 0.003), presence of delayed central enhancement (p = 0.064), presence of targetoid restriction (p = 0.086), presence of satellite nodules (p = 0.050), presence of portal and hepatic vein tumor thrombus (p = 0.035), and presence of peritumoral enhancement (p = 0.030) were potential differential factors for the prediction of HCC with Ki-67 ≥ 30%.Subsequently, multivariate logistic analysis was conducted on these potential differential factors; no factors were significantly different (Fig. 2).

Discussion
Our study showed that the presence of mosaic architecture, infiltrative appearance, and absence of targetoid HBP were independent predictors of Ki-67 (Ki-67 index ≥ 50%) positivity in patients with HCC.A noninvasive multivariable model composed of three LI-RADS features was developed to predict the Ki-67 index in patients with HCC; the model showed good discriminative performance with an AUC of 0.776, and this may be  an effective imaging approach for the risk stratification of patients with HCC.Many studies have confirmed that high Ki-67 expression levels are associated with tumor invasiveness and poor prognoses in patients with HCC [20,23].However, there is still no consensus about the precise cut-off value for Ki-67 because values ranging from 5 to 50% are used yet [6,[24][25][26][27][28].To date, no studies have evaluated the correlation between MRI LI-RADS features and different Ki-67 expression.In the present study, LI-RADS features were compared between the low and high Ki-67 index groups (Ki-67 index ≥ 20% vs. < 20%; Ki-67 index ≥ 30% vs. < 30%; Ki-67 index ≥ 50% vs. < 50%), which demonstrated that there were no LI-RADS features showing statistically significant differences in predicting Ki-67 cut-off values of 20% (Ki-67 index ≥ 20% vs. < 20%) and 30% (Ki-67 index ≥ 30% vs. < 30%).A possible explanation is that although the tumors with different levels of Ki-67 expression may have different components and tissue structures, which may be overlapped with imaging findings in HCC.Thus, more prospective studies with a larger sample size are needed to confirm this result in the future.
In this study, the results also showed that the LI-RADS features including mosaic architecture, infiltrative appearance, and targetoid HBP are sensitive in predicting high Ki-67 expression (Ki-67 index ≥ 50% vs. < 50%) in patients with HCC.Mosaic architecture is a well-known ancillary feature of HCC characterized by random internal nodules or components of different attenuations, intensities, enhancements, sizes, shapes, and separation by fibrous material within tumors [29].Mosaic architecture may reflect tumor heterogeneity, corresponding to histological variations, including tumor viability, fatty infiltration, necrosis, hemorrhage, cystic degeneration, or copper deposition, suggesting that the internal components of HCCs are complex [30].It is more common in progressed HCC rather than early HCC [29].The results of the our study are consistent with the study by Liu [18].
Infiltrative appearance and targetoid HBP are uncommon in HCC and more common in cholangiocarcinoma.Infiltrative appearance was observed in approximately 8%-20% of all HCC cases [31].Ki-67-positive HCCs have a more infiltrative appearance than conventional Ki-67-negative HCCs.Thus, infiltrative appearance is a key feature of Ki-67-positive HCCs, which may represent true infiltration of tumor cells into the liver parenchyma, and has been associated with more aggressive tumors, metastasis, and short survival times [32,33].Targetoid HBP was rarely observed in HCC in our study, especially in Ki-67-positive HCCs.However, it was more frequently observed in CK19-positive HCCs, which suggests the tumor progenitor phenotype [34].
Several studies have evaluated the diagnostic value of the different models for predicting Ki-67 expression [15,[17][18][19]35], however, most of the studies are on the basis of radiomics.Wu et al. conducted a radiomics nomogram based on CT features, AFP, and Edmondson grades to predict high Ki-67 expression (≥ 20%) with AUCs of 0.884 and 0.819 in the training and validation groups, respectively [19].Fan developed a combined model including artery phase Rad-scores and serum AFP levels based on enhanced MRI to predict high Ki-67 expression (≥ 14%) in HCC, which performed better than the artery phase radiomics model in the training (AUC: 0.922 vs. 0.873) and validation cohorts (AUC: 0.863 vs. 0.813) [15].Undoubtedly, the above previous studies indicated that radiomics was important for predicting Ki-67 expression [19]; however, it requires large sample sizes and is time-consuming.Thus, the present study is the first one to develop a preliminary multivariable model based on LI-RADS features for individualized discrimination of high-level Ki-67 HCCs.The developed model, which included mosaic architecture, infiltrative appearance, and targetoid HBP, was proved to be the best predictive combination with an AUC of 0.776.This model is clinically significant because it is simple and user-friendly, which enables clinicians to implement it.
Our study has several limitations.First, there was the potential for selection bias due to the study being a retrospective, single-center study.Second, the study was limited by the small sample size, and a prospective study with more cases is needed.Third, three non-high-risk patients were included in our study, which may affect the result because LI-RADS version 2018 specifically defined high-risk patients.Finally, a multivariable model was built for the prediction of Ki-67 expression; however, the performance and reproducibility of the model requires further testing using additional methods of external validation due to the limited number of cases.
In conclusion, our study showed that the presence of mosaic, infiltrative appearance, and the absence of targetoid HBP are independent predictors of Ki-67 indexes ≥ 50% in patients with HCC.A noninvasive multivariable model composed of three LI-RADS features was developed to predict the Ki-67 index in patients with HCC, which showed good discriminative performance, with an AUC of 0.776, and may be an effective imaging approach for the risk stratification in patients with HCC.

Fig. 1
Fig. 1 Flow chart of the study population selection

Fig. 4
Fig.4 The ROC curve of the models.Model A: mosaic architecture and infiltrated appearance; Model B: mosaic architecture and targetoid hepatobiliary phase; Model C: infiltrated appearance and targetoid HBP; Model D: mosaic architecture, infiltrated appearance and targetoid HBP

Table 1
Patient characteristics Abbreviations: AFP alpha-fetoprotein, ALT alanine aminotransferase, HBV Hepatitis B, M mean, PA plasma albumin, PBC primary biliary cirrhosis, SD standard deviation, STB serum total bilirubin

Table 2
The MRI imaging features of patients with different cut-off value of Ki-67

Table 3
Multivariate analysis with logistic regression in the MRI imaging featuresAbbreviations: APHE arterial phase hyperenhancement, B regression coefficients, CI confidence interval, HBP hepatobiliary phase, OR odds ratio * p < 0.05 * p < 0.05

Table 4
Predictive performance of the modelAbbreviations: AUC the areas under the receiver operating characteristic curves (AUC), CI confidence interval, HBP hepatobiliary phase

Table 5
Comparison of ROCs in predicting models using the Delong testAbbreviations: Model A: mosaic architecture and infiltrated appearance; Model B: mosaic architecture and targetoid HBP; Model C: infiltrated appearance and targetoid HBP; Model D: mosaic architecture, infiltrated appearance and targetoid HBP